Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
This work addresses perception failures in challenging situations for learning agents, but it is incremental as it builds on existing single-frame models.
The authors tackled the problem of robust environment representation for learning agents by extending a single-frame segmentation model to handle multiple frames with a parameter-efficient temporal filtering concept, resulting in a hierarchical recurrent architecture that decouples temporal dependencies from scene representation and demonstrated improved ability to cope with data perturbations on a synthetic dataset.
Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail in challenging situations. These failures are often not solvable on the basis of a single image. In this work, we present a parameter-efficient temporal filtering concept which extends an existing single-frame segmentation model to work with multiple frames. The resulting recurrent architecture temporally filters representations on all abstraction levels in a hierarchical manner, while decoupling temporal dependencies from scene representation. Using a synthetic dataset, we show the ability of our model to cope with data perturbations and highlight the importance of recurrent and hierarchical filtering.